{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PATH = \"/Users/alexcombs/Desktop/iris/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "r = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open(PATH + 'iris.data', 'w') as f:\n",
    "    f.write(r.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iris.data']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "os.chdir(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(PATH + 'iris.data', names=[\"sepal length\", \"sepal width\", \"petal length\", \"petal width\", \"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width  petal length  petal width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "5      5.4\n",
       "6      4.6\n",
       "7      5.0\n",
       "8      4.4\n",
       "9      4.9\n",
       "10     5.4\n",
       "11     4.8\n",
       "12     4.8\n",
       "13     4.3\n",
       "14     5.8\n",
       "15     5.7\n",
       "16     5.4\n",
       "17     5.1\n",
       "18     5.7\n",
       "19     5.1\n",
       "20     5.4\n",
       "21     5.1\n",
       "22     4.6\n",
       "23     5.1\n",
       "24     4.8\n",
       "25     5.0\n",
       "26     5.0\n",
       "27     5.2\n",
       "28     5.2\n",
       "29     4.7\n",
       "      ... \n",
       "120    6.9\n",
       "121    5.6\n",
       "122    7.7\n",
       "123    6.3\n",
       "124    6.7\n",
       "125    7.2\n",
       "126    6.2\n",
       "127    6.1\n",
       "128    6.4\n",
       "129    7.2\n",
       "130    7.4\n",
       "131    7.9\n",
       "132    6.4\n",
       "133    6.3\n",
       "134    6.1\n",
       "135    7.7\n",
       "136    6.3\n",
       "137    6.4\n",
       "138    6.0\n",
       "139    6.9\n",
       "140    6.7\n",
       "141    6.9\n",
       "142    5.8\n",
       "143    6.8\n",
       "144    6.7\n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: sepal length, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sepal length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length  sepal width\n",
       "0           5.1          3.5\n",
       "1           4.9          3.0\n",
       "2           4.7          3.2\n",
       "3           4.6          3.1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[:3,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal width  petal width\n",
       "0          3.5          0.2\n",
       "1          3.0          0.2\n",
       "2          3.2          0.2\n",
       "3          3.1          0.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[:3, [x for x in df.columns if 'width' in x]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "5             5.4          3.9           1.7          0.4     Iris-setosa\n",
       "6             4.6          3.4           1.4          0.3     Iris-setosa\n",
       "7             5.0          3.4           1.5          0.2     Iris-setosa\n",
       "8             4.4          2.9           1.4          0.2     Iris-setosa\n",
       "9             4.9          3.1           1.5          0.1     Iris-setosa\n",
       "10            5.4          3.7           1.5          0.2     Iris-setosa\n",
       "11            4.8          3.4           1.6          0.2     Iris-setosa\n",
       "12            4.8          3.0           1.4          0.1     Iris-setosa\n",
       "13            4.3          3.0           1.1          0.1     Iris-setosa\n",
       "14            5.8          4.0           1.2          0.2     Iris-setosa\n",
       "15            5.7          4.4           1.5          0.4     Iris-setosa\n",
       "16            5.4          3.9           1.3          0.4     Iris-setosa\n",
       "17            5.1          3.5           1.4          0.3     Iris-setosa\n",
       "18            5.7          3.8           1.7          0.3     Iris-setosa\n",
       "19            5.1          3.8           1.5          0.3     Iris-setosa\n",
       "20            5.4          3.4           1.7          0.2     Iris-setosa\n",
       "21            5.1          3.7           1.5          0.4     Iris-setosa\n",
       "22            4.6          3.6           1.0          0.2     Iris-setosa\n",
       "23            5.1          3.3           1.7          0.5     Iris-setosa\n",
       "24            4.8          3.4           1.9          0.2     Iris-setosa\n",
       "25            5.0          3.0           1.6          0.2     Iris-setosa\n",
       "26            5.0          3.4           1.6          0.4     Iris-setosa\n",
       "27            5.2          3.5           1.5          0.2     Iris-setosa\n",
       "28            5.2          3.4           1.4          0.2     Iris-setosa\n",
       "29            4.7          3.2           1.6          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "120           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "121           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "122           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "123           6.3          2.7           4.9          1.8  Iris-virginica\n",
       "124           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "125           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "126           6.2          2.8           4.8          1.8  Iris-virginica\n",
       "127           6.1          3.0           4.9          1.8  Iris-virginica\n",
       "128           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "129           7.2          3.0           5.8          1.6  Iris-virginica\n",
       "130           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "131           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "132           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "133           6.3          2.8           5.1          1.5  Iris-virginica\n",
       "134           6.1          2.6           5.6          1.4  Iris-virginica\n",
       "135           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "136           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "137           6.4          3.1           5.5          1.8  Iris-virginica\n",
       "138           6.0          3.0           4.8          1.8  Iris-virginica\n",
       "139           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "140           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "141           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "142           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "143           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "144           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\n",
       "100           6.3          3.3           6.0          2.5  Iris-virginica\n",
       "101           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "102           7.1          3.0           5.9          2.1  Iris-virginica\n",
       "103           6.3          2.9           5.6          1.8  Iris-virginica\n",
       "104           6.5          3.0           5.8          2.2  Iris-virginica\n",
       "105           7.6          3.0           6.6          2.1  Iris-virginica\n",
       "106           4.9          2.5           4.5          1.7  Iris-virginica\n",
       "107           7.3          2.9           6.3          1.8  Iris-virginica\n",
       "108           6.7          2.5           5.8          1.8  Iris-virginica\n",
       "109           7.2          3.6           6.1          2.5  Iris-virginica\n",
       "110           6.5          3.2           5.1          2.0  Iris-virginica\n",
       "111           6.4          2.7           5.3          1.9  Iris-virginica\n",
       "112           6.8          3.0           5.5          2.1  Iris-virginica\n",
       "113           5.7          2.5           5.0          2.0  Iris-virginica\n",
       "114           5.8          2.8           5.1          2.4  Iris-virginica\n",
       "115           6.4          3.2           5.3          2.3  Iris-virginica\n",
       "116           6.5          3.0           5.5          1.8  Iris-virginica\n",
       "117           7.7          3.8           6.7          2.2  Iris-virginica\n",
       "118           7.7          2.6           6.9          2.3  Iris-virginica\n",
       "119           6.0          2.2           5.0          1.5  Iris-virginica\n",
       "120           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "121           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "122           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "123           6.3          2.7           4.9          1.8  Iris-virginica\n",
       "124           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "125           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "126           6.2          2.8           4.8          1.8  Iris-virginica\n",
       "127           6.1          3.0           4.9          1.8  Iris-virginica\n",
       "128           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "129           7.2          3.0           5.8          1.6  Iris-virginica\n",
       "130           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "131           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "132           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "133           6.3          2.8           5.1          1.5  Iris-virginica\n",
       "134           6.1          2.6           5.6          1.4  Iris-virginica\n",
       "135           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "136           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "137           6.4          3.1           5.5          1.8  Iris-virginica\n",
       "138           6.0          3.0           4.8          1.8  Iris-virginica\n",
       "139           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "140           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "141           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "142           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "143           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "144           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['class']=='Iris-virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length    150\n",
       "sepal width     150\n",
       "petal length    150\n",
       "petal width     150\n",
       "class           150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length    50\n",
       "sepal width     50\n",
       "petal length    50\n",
       "petal width     50\n",
       "class           50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['class']=='Iris-virginica'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sepal length  sepal width  petal length  petal width           class\n",
       "0            6.3          3.3           6.0          2.5  Iris-virginica\n",
       "1            5.8          2.7           5.1          1.9  Iris-virginica\n",
       "2            7.1          3.0           5.9          2.1  Iris-virginica\n",
       "3            6.3          2.9           5.6          1.8  Iris-virginica\n",
       "4            6.5          3.0           5.8          2.2  Iris-virginica\n",
       "5            7.6          3.0           6.6          2.1  Iris-virginica\n",
       "6            4.9          2.5           4.5          1.7  Iris-virginica\n",
       "7            7.3          2.9           6.3          1.8  Iris-virginica\n",
       "8            6.7          2.5           5.8          1.8  Iris-virginica\n",
       "9            7.2          3.6           6.1          2.5  Iris-virginica\n",
       "10           6.5          3.2           5.1          2.0  Iris-virginica\n",
       "11           6.4          2.7           5.3          1.9  Iris-virginica\n",
       "12           6.8          3.0           5.5          2.1  Iris-virginica\n",
       "13           5.7          2.5           5.0          2.0  Iris-virginica\n",
       "14           5.8          2.8           5.1          2.4  Iris-virginica\n",
       "15           6.4          3.2           5.3          2.3  Iris-virginica\n",
       "16           6.5          3.0           5.5          1.8  Iris-virginica\n",
       "17           7.7          3.8           6.7          2.2  Iris-virginica\n",
       "18           7.7          2.6           6.9          2.3  Iris-virginica\n",
       "19           6.0          2.2           5.0          1.5  Iris-virginica\n",
       "20           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "21           5.6          2.8           4.9          2.0  Iris-virginica\n",
       "22           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "23           6.3          2.7           4.9          1.8  Iris-virginica\n",
       "24           6.7          3.3           5.7          2.1  Iris-virginica\n",
       "25           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "26           6.2          2.8           4.8          1.8  Iris-virginica\n",
       "27           6.1          3.0           4.9          1.8  Iris-virginica\n",
       "28           6.4          2.8           5.6          2.1  Iris-virginica\n",
       "29           7.2          3.0           5.8          1.6  Iris-virginica\n",
       "30           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "31           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "32           6.4          2.8           5.6          2.2  Iris-virginica\n",
       "33           6.3          2.8           5.1          1.5  Iris-virginica\n",
       "34           6.1          2.6           5.6          1.4  Iris-virginica\n",
       "35           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "36           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "37           6.4          3.1           5.5          1.8  Iris-virginica\n",
       "38           6.0          3.0           4.8          1.8  Iris-virginica\n",
       "39           6.9          3.1           5.4          2.1  Iris-virginica\n",
       "40           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "41           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "42           5.8          2.7           5.1          1.9  Iris-virginica\n",
       "43           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "44           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "45           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "46           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "47           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "48           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "49           5.9          3.0           5.1          1.8  Iris-virginica"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virginica = df[df['class']=='Iris-virginica'].reset_index(drop=True)\n",
    "virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width           class\n",
       "100           6.3          3.3           6.0          2.5  Iris-virginica\n",
       "109           7.2          3.6           6.1          2.5  Iris-virginica\n",
       "114           5.8          2.8           5.1          2.4  Iris-virginica\n",
       "115           6.4          3.2           5.3          2.3  Iris-virginica\n",
       "118           7.7          2.6           6.9          2.3  Iris-virginica\n",
       "120           6.9          3.2           5.7          2.3  Iris-virginica\n",
       "135           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "136           6.3          3.4           5.6          2.4  Iris-virginica\n",
       "140           6.7          3.1           5.6          2.4  Iris-virginica\n",
       "141           6.9          3.1           5.1          2.3  Iris-virginica\n",
       "143           6.8          3.2           5.9          2.3  Iris-virginica\n",
       "144           6.7          3.3           5.7          2.5  Iris-virginica\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['class']=='Iris-virginica')&(df['petal width']>2.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length  sepal width  petal length  petal width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40%</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>1.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80%</th>\n",
       "      <td>6.520000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5.320000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>6.900000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>7.255000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>2.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length  sepal width  petal length  petal width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "20%        5.000000     2.700000      1.500000     0.200000\n",
       "40%        5.600000     3.000000      3.900000     1.160000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "80%        6.520000     3.400000      5.320000     1.900000\n",
       "90%        6.900000     3.610000      5.800000     2.200000\n",
       "95%        7.255000     3.800000      6.100000     2.300000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(percentiles=[.20,.40,.80,.90,.95])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal length  sepal width  petal length  petal width\n",
       "sepal length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10bbaf390>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PD1M25iJarVZX6p2Nmt4XIyMjtbff5P7plqYfF1VqtVq4+8qy+xX+qr27iSJzOXCPu39u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baf46d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "ax.hist(df['petal width'], color='black');\n",
    "ax.set_ylabel('Count', fontsize=12)\n",
    "ax.set_xlabel('Width', fontsize=12)\n",
    "plt.title('Iris Petal Width', fontsize=14, y=1.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XFk00CRvROwhVfRO4HzgW7+qrcE8oW+15VJgNXQtMFpE1w9bYh1ddNhcY0xSl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc7feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize=(6,4))\n",
    "\n",
    "ax[0][0].hist(df['petal width'], color='black');\n",
    "ax[0][0].set_ylabel('Count', fontsize=12)\n",
    "ax[0][0].set_xlabel('Width', fontsize=12)\n",
    "ax[0][0].set_title('Iris Petal Width', fontsize=14, y=1.01)\n",
    "\n",
    "ax[0][1].hist(df['petal length'], color='black');\n",
    "ax[0][1].set_ylabel('Count', fontsize=12)\n",
    "ax[0][1].set_xlabel('Lenth', fontsize=12)\n",
    "ax[0][1].set_title('Iris Petal Lenth', fontsize=14, y=1.01)\n",
    "\n",
    "ax[1][0].hist(df['sepal width'], color='black');\n",
    "ax[1][0].set_ylabel('Count', fontsize=12)\n",
    "ax[1][0].set_xlabel('Width', fontsize=12)\n",
    "ax[1][0].set_title('Iris Sepal Width', fontsize=14, y=1.01)\n",
    "\n",
    "ax[1][1].hist(df['sepal length'], color='black');\n",
    "ax[1][1].set_ylabel('Count', fontsize=12)\n",
    "ax[1][1].set_xlabel('Length', fontsize=12)\n",
    "ax[1][1].set_title('Iris Sepal Length', fontsize=14, y=1.01)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c094128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JWknhUeC6suXfDViOyNirOenjsA7oTM57s7tfnT1UERFptlyn4zSzw9x9j5mN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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc5a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "ax.scatter(df['petal width'],df['petal length'], color='green')\n",
    "ax.set_xlabel('Petal Width')\n",
    "ax.set_ylabel('Petal Length')\n",
    "ax.set_title('Petal Scatterplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c0bda20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QzAGCxIEghhRZbRyJiIpnf+LQPOYgLQdzW05vyyGdFrUB3mNxLC2ZrbaTSWB5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\nQfaQuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0a2588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "ax.plot(df['petal length'], color='blue')\n",
    "ax.set_xlabel('Specimen Number')\n",
    "ax.set_ylabel('Petal Length')\n",
    "ax.set_title('Petal Length Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10c610eb8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJGJK4iIiEVMSFxGJmJK4iEjElMRFRCKmJC4iEjElcRGRiCmJi4hETElcRCRi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4d5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,6))\n",
    "bar_width = .8\n",
    "labels = [x for x in df.columns if 'length' in x or 'width' in x]\n",
    "ver_y = [df[df['class']=='Iris-versicolor'][x].mean() for x in labels]\n",
    "vir_y = [df[df['class']=='Iris-virginica'][x].mean() for x in labels]\n",
    "set_y = [df[df['class']=='Iris-setosa'][x].mean() for x in labels]\n",
    "x = np.arange(len(labels))\n",
    "ax.bar(x, vir_y, bar_width, bottom=set_y, color='darkgrey')\n",
    "ax.bar(x, set_y, bar_width, bottom=ver_y, color='white')\n",
    "ax.bar(x, ver_y, bar_width, color='black')\n",
    "ax.set_xticks(x + (bar_width/2))\n",
    "ax.set_xticklabels(labels, rotation=-70, fontsize=12);\n",
    "ax.set_title('Mean Feature Measurement By Class', y=1.01)\n",
    "ax.legend(['Virginica','Setosa','Versicolor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x10c4ec6d8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAXALarKNDaheU3Rjez6YyCjBgptsYwOqV984vmCacHsnoWndqV8l4hHljs3k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VAAAg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\nOvRROiX6mdpJqGwhPM1TDRV3owEKIYSouGJDCbtO/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AxEIuC5fx4UXt97pBRDUwxXUyzT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\nYcMYY4wxxhgb8fjChjHGGGOMMTbi/R9aynYqgjSt5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c0b5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df, hue=\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mdF1CdjsXL54HIDU12cWRNK6iotIn7co1G3SXkri7fTZqfdLOzs7G87L5rDqd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PdA8WiN4hmE4hWC0iLUeqbcGNVYiFfWNoYNs+R8YmIiRUVFDRZt68+pKlQWoJZeQS3L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\nzZXrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10eb3b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(7, 7))\n",
    "sns.set(style='white', palette='muted')\n",
    "sns.violinplot(x=df['class'], y=df['sepal length'], ax=ax[0,0])\n",
    "sns.violinplot(x=df['class'], y=df['sepal width'], ax=ax[0,1])\n",
    "sns.violinplot(x=df['class'], y=df['petal length'], ax=ax[1,0])\n",
    "sns.violinplot(x=df['class'], y=df['petal width'], ax=ax[1,1])\n",
    "fig.suptitle('Violin Plots', fontsize=16, y=1.03)\n",
    "for i in ax.flat:\n",
    "    plt.setp(i.get_xticklabels(), rotation=-90)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width class\n",
       "0             5.1          3.5           1.4          0.2   SET\n",
       "1             4.9          3.0           1.4          0.2   SET\n",
       "2             4.7          3.2           1.3          0.2   SET\n",
       "3             4.6          3.1           1.5          0.2   SET\n",
       "4             5.0          3.6           1.4          0.2   SET\n",
       "5             5.4          3.9           1.7          0.4   SET\n",
       "6             4.6          3.4           1.4          0.3   SET\n",
       "7             5.0          3.4           1.5          0.2   SET\n",
       "8             4.4          2.9           1.4          0.2   SET\n",
       "9             4.9          3.1           1.5          0.1   SET\n",
       "10            5.4          3.7           1.5          0.2   SET\n",
       "11            4.8          3.4           1.6          0.2   SET\n",
       "12            4.8          3.0           1.4          0.1   SET\n",
       "13            4.3          3.0           1.1          0.1   SET\n",
       "14            5.8          4.0           1.2          0.2   SET\n",
       "15            5.7          4.4           1.5          0.4   SET\n",
       "16            5.4          3.9           1.3          0.4   SET\n",
       "17            5.1          3.5           1.4          0.3   SET\n",
       "18            5.7          3.8           1.7          0.3   SET\n",
       "19            5.1          3.8           1.5          0.3   SET\n",
       "20            5.4          3.4           1.7          0.2   SET\n",
       "21            5.1          3.7           1.5          0.4   SET\n",
       "22            4.6          3.6           1.0          0.2   SET\n",
       "23            5.1          3.3           1.7          0.5   SET\n",
       "24            4.8          3.4           1.9          0.2   SET\n",
       "25            5.0          3.0           1.6          0.2   SET\n",
       "26            5.0          3.4           1.6          0.4   SET\n",
       "27            5.2          3.5           1.5          0.2   SET\n",
       "28            5.2          3.4           1.4          0.2   SET\n",
       "29            4.7          3.2           1.6          0.2   SET\n",
       "..            ...          ...           ...          ...   ...\n",
       "120           6.9          3.2           5.7          2.3   VIR\n",
       "121           5.6          2.8           4.9          2.0   VIR\n",
       "122           7.7          2.8           6.7          2.0   VIR\n",
       "123           6.3          2.7           4.9          1.8   VIR\n",
       "124           6.7          3.3           5.7          2.1   VIR\n",
       "125           7.2          3.2           6.0          1.8   VIR\n",
       "126           6.2          2.8           4.8          1.8   VIR\n",
       "127           6.1          3.0           4.9          1.8   VIR\n",
       "128           6.4          2.8           5.6          2.1   VIR\n",
       "129           7.2          3.0           5.8          1.6   VIR\n",
       "130           7.4          2.8           6.1          1.9   VIR\n",
       "131           7.9          3.8           6.4          2.0   VIR\n",
       "132           6.4          2.8           5.6          2.2   VIR\n",
       "133           6.3          2.8           5.1          1.5   VIR\n",
       "134           6.1          2.6           5.6          1.4   VIR\n",
       "135           7.7          3.0           6.1          2.3   VIR\n",
       "136           6.3          3.4           5.6          2.4   VIR\n",
       "137           6.4          3.1           5.5          1.8   VIR\n",
       "138           6.0          3.0           4.8          1.8   VIR\n",
       "139           6.9          3.1           5.4          2.1   VIR\n",
       "140           6.7          3.1           5.6          2.4   VIR\n",
       "141           6.9          3.1           5.1          2.3   VIR\n",
       "142           5.8          2.7           5.1          1.9   VIR\n",
       "143           6.8          3.2           5.9          2.3   VIR\n",
       "144           6.7          3.3           5.7          2.5   VIR\n",
       "145           6.7          3.0           5.2          2.3   VIR\n",
       "146           6.3          2.5           5.0          1.9   VIR\n",
       "147           6.5          3.0           5.2          2.0   VIR\n",
       "148           6.2          3.4           5.4          2.3   VIR\n",
       "149           5.9          3.0           5.1          1.8   VIR\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'] = df['class'].map({'Iris-setosa':'SET', 'Iris-virginica': 'VIR', 'Iris-versicolor': 'VER'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "      <th>wide petal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width class  wide petal\n",
       "0             5.1          3.5           1.4          0.2   SET           0\n",
       "1             4.9          3.0           1.4          0.2   SET           0\n",
       "2             4.7          3.2           1.3          0.2   SET           0\n",
       "3             4.6          3.1           1.5          0.2   SET           0\n",
       "4             5.0          3.6           1.4          0.2   SET           0\n",
       "5             5.4          3.9           1.7          0.4   SET           0\n",
       "6             4.6          3.4           1.4          0.3   SET           0\n",
       "7             5.0          3.4           1.5          0.2   SET           0\n",
       "8             4.4          2.9           1.4          0.2   SET           0\n",
       "9             4.9          3.1           1.5          0.1   SET           0\n",
       "10            5.4          3.7           1.5          0.2   SET           0\n",
       "11            4.8          3.4           1.6          0.2   SET           0\n",
       "12            4.8          3.0           1.4          0.1   SET           0\n",
       "13            4.3          3.0           1.1          0.1   SET           0\n",
       "14            5.8          4.0           1.2          0.2   SET           0\n",
       "15            5.7          4.4           1.5          0.4   SET           0\n",
       "16            5.4          3.9           1.3          0.4   SET           0\n",
       "17            5.1          3.5           1.4          0.3   SET           0\n",
       "18            5.7          3.8           1.7          0.3   SET           0\n",
       "19            5.1          3.8           1.5          0.3   SET           0\n",
       "20            5.4          3.4           1.7          0.2   SET           0\n",
       "21            5.1          3.7           1.5          0.4   SET           0\n",
       "22            4.6          3.6           1.0          0.2   SET           0\n",
       "23            5.1          3.3           1.7          0.5   SET           0\n",
       "24            4.8          3.4           1.9          0.2   SET           0\n",
       "25            5.0          3.0           1.6          0.2   SET           0\n",
       "26            5.0          3.4           1.6          0.4   SET           0\n",
       "27            5.2          3.5           1.5          0.2   SET           0\n",
       "28            5.2          3.4           1.4          0.2   SET           0\n",
       "29            4.7          3.2           1.6          0.2   SET           0\n",
       "..            ...          ...           ...          ...   ...         ...\n",
       "120           6.9          3.2           5.7          2.3   VIR           1\n",
       "121           5.6          2.8           4.9          2.0   VIR           1\n",
       "122           7.7          2.8           6.7          2.0   VIR           1\n",
       "123           6.3          2.7           4.9          1.8   VIR           1\n",
       "124           6.7          3.3           5.7          2.1   VIR           1\n",
       "125           7.2          3.2           6.0          1.8   VIR           1\n",
       "126           6.2          2.8           4.8          1.8   VIR           1\n",
       "127           6.1          3.0           4.9          1.8   VIR           1\n",
       "128           6.4          2.8           5.6          2.1   VIR           1\n",
       "129           7.2          3.0           5.8          1.6   VIR           1\n",
       "130           7.4          2.8           6.1          1.9   VIR           1\n",
       "131           7.9          3.8           6.4          2.0   VIR           1\n",
       "132           6.4          2.8           5.6          2.2   VIR           1\n",
       "133           6.3          2.8           5.1          1.5   VIR           1\n",
       "134           6.1          2.6           5.6          1.4   VIR           1\n",
       "135           7.7          3.0           6.1          2.3   VIR           1\n",
       "136           6.3          3.4           5.6          2.4   VIR           1\n",
       "137           6.4          3.1           5.5          1.8   VIR           1\n",
       "138           6.0          3.0           4.8          1.8   VIR           1\n",
       "139           6.9          3.1           5.4          2.1   VIR           1\n",
       "140           6.7          3.1           5.6          2.4   VIR           1\n",
       "141           6.9          3.1           5.1          2.3   VIR           1\n",
       "142           5.8          2.7           5.1          1.9   VIR           1\n",
       "143           6.8          3.2           5.9          2.3   VIR           1\n",
       "144           6.7          3.3           5.7          2.5   VIR           1\n",
       "145           6.7          3.0           5.2          2.3   VIR           1\n",
       "146           6.3          2.5           5.0          1.9   VIR           1\n",
       "147           6.5          3.0           5.2          2.0   VIR           1\n",
       "148           6.2          3.4           5.4          2.3   VIR           1\n",
       "149           5.9          3.0           5.1          1.8   VIR           1\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['wide petal'] = df['petal width'].apply(lambda v: 1 if v >= 1.3 else 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "      <th>wide petal</th>\n",
       "      <th>petal area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>SET</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>SET</td>\n",
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       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
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       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>10.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>12.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>12.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>7.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>14.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>8.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>13.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>14.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>11.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>12.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>9.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width class  wide petal  \\\n",
       "0             5.1          3.5           1.4          0.2   SET           0   \n",
       "1             4.9          3.0           1.4          0.2   SET           0   \n",
       "2             4.7          3.2           1.3          0.2   SET           0   \n",
       "3             4.6          3.1           1.5          0.2   SET           0   \n",
       "4             5.0          3.6           1.4          0.2   SET           0   \n",
       "5             5.4          3.9           1.7          0.4   SET           0   \n",
       "6             4.6          3.4           1.4          0.3   SET           0   \n",
       "7             5.0          3.4           1.5          0.2   SET           0   \n",
       "8             4.4          2.9           1.4          0.2   SET           0   \n",
       "9             4.9          3.1           1.5          0.1   SET           0   \n",
       "10            5.4          3.7           1.5          0.2   SET           0   \n",
       "11            4.8          3.4           1.6          0.2   SET           0   \n",
       "12            4.8          3.0           1.4          0.1   SET           0   \n",
       "13            4.3          3.0           1.1          0.1   SET           0   \n",
       "14            5.8          4.0           1.2          0.2   SET           0   \n",
       "15            5.7          4.4           1.5          0.4   SET           0   \n",
       "16            5.4          3.9           1.3          0.4   SET           0   \n",
       "17            5.1          3.5           1.4          0.3   SET           0   \n",
       "18            5.7          3.8           1.7          0.3   SET           0   \n",
       "19            5.1          3.8           1.5          0.3   SET           0   \n",
       "20            5.4          3.4           1.7          0.2   SET           0   \n",
       "21            5.1          3.7           1.5          0.4   SET           0   \n",
       "22            4.6          3.6           1.0          0.2   SET           0   \n",
       "23            5.1          3.3           1.7          0.5   SET           0   \n",
       "24            4.8          3.4           1.9          0.2   SET           0   \n",
       "25            5.0          3.0           1.6          0.2   SET           0   \n",
       "26            5.0          3.4           1.6          0.4   SET           0   \n",
       "27            5.2          3.5           1.5          0.2   SET           0   \n",
       "28            5.2          3.4           1.4          0.2   SET           0   \n",
       "29            4.7          3.2           1.6          0.2   SET           0   \n",
       "..            ...          ...           ...          ...   ...         ...   \n",
       "120           6.9          3.2           5.7          2.3   VIR           1   \n",
       "121           5.6          2.8           4.9          2.0   VIR           1   \n",
       "122           7.7          2.8           6.7          2.0   VIR           1   \n",
       "123           6.3          2.7           4.9          1.8   VIR           1   \n",
       "124           6.7          3.3           5.7          2.1   VIR           1   \n",
       "125           7.2          3.2           6.0          1.8   VIR           1   \n",
       "126           6.2          2.8           4.8          1.8   VIR           1   \n",
       "127           6.1          3.0           4.9          1.8   VIR           1   \n",
       "128           6.4          2.8           5.6          2.1   VIR           1   \n",
       "129           7.2          3.0           5.8          1.6   VIR           1   \n",
       "130           7.4          2.8           6.1          1.9   VIR           1   \n",
       "131           7.9          3.8           6.4          2.0   VIR           1   \n",
       "132           6.4          2.8           5.6          2.2   VIR           1   \n",
       "133           6.3          2.8           5.1          1.5   VIR           1   \n",
       "134           6.1          2.6           5.6          1.4   VIR           1   \n",
       "135           7.7          3.0           6.1          2.3   VIR           1   \n",
       "136           6.3          3.4           5.6          2.4   VIR           1   \n",
       "137           6.4          3.1           5.5          1.8   VIR           1   \n",
       "138           6.0          3.0           4.8          1.8   VIR           1   \n",
       "139           6.9          3.1           5.4          2.1   VIR           1   \n",
       "140           6.7          3.1           5.6          2.4   VIR           1   \n",
       "141           6.9          3.1           5.1          2.3   VIR           1   \n",
       "142           5.8          2.7           5.1          1.9   VIR           1   \n",
       "143           6.8          3.2           5.9          2.3   VIR           1   \n",
       "144           6.7          3.3           5.7          2.5   VIR           1   \n",
       "145           6.7          3.0           5.2          2.3   VIR           1   \n",
       "146           6.3          2.5           5.0          1.9   VIR           1   \n",
       "147           6.5          3.0           5.2          2.0   VIR           1   \n",
       "148           6.2          3.4           5.4          2.3   VIR           1   \n",
       "149           5.9          3.0           5.1          1.8   VIR           1   \n",
       "\n",
       "     petal area  \n",
       "0          0.28  \n",
       "1          0.28  \n",
       "2          0.26  \n",
       "3          0.30  \n",
       "4          0.28  \n",
       "5          0.68  \n",
       "6          0.42  \n",
       "7          0.30  \n",
       "8          0.28  \n",
       "9          0.15  \n",
       "10         0.30  \n",
       "11         0.32  \n",
       "12         0.14  \n",
       "13         0.11  \n",
       "14         0.24  \n",
       "15         0.60  \n",
       "16         0.52  \n",
       "17         0.42  \n",
       "18         0.51  \n",
       "19         0.45  \n",
       "20         0.34  \n",
       "21         0.60  \n",
       "22         0.20  \n",
       "23         0.85  \n",
       "24         0.38  \n",
       "25         0.32  \n",
       "26         0.64  \n",
       "27         0.30  \n",
       "28         0.28  \n",
       "29         0.32  \n",
       "..          ...  \n",
       "120       13.11  \n",
       "121        9.80  \n",
       "122       13.40  \n",
       "123        8.82  \n",
       "124       11.97  \n",
       "125       10.80  \n",
       "126        8.64  \n",
       "127        8.82  \n",
       "128       11.76  \n",
       "129        9.28  \n",
       "130       11.59  \n",
       "131       12.80  \n",
       "132       12.32  \n",
       "133        7.65  \n",
       "134        7.84  \n",
       "135       14.03  \n",
       "136       13.44  \n",
       "137        9.90  \n",
       "138        8.64  \n",
       "139       11.34  \n",
       "140       13.44  \n",
       "141       11.73  \n",
       "142        9.69  \n",
       "143       13.57  \n",
       "144       14.25  \n",
       "145       11.96  \n",
       "146        9.50  \n",
       "147       10.40  \n",
       "148       12.42  \n",
       "149        9.18  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['petal area'] = df.apply(lambda r: r['petal length'] * r['petal width'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "      <th>wide petal</th>\n",
       "      <th>petal area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.629241</td>\n",
       "      <td>1.252763</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.272966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.589235</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.272966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.547563</td>\n",
       "      <td>1.163151</td>\n",
       "      <td>0.262364</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.347074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.526056</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.203973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.280934</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.272966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.686399</td>\n",
       "      <td>1.360977</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>-0.916291</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.385662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.526056</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.203973</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.867501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.203973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.481605</td>\n",
       "      <td>1.064711</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.272966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.589235</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.897120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.686399</td>\n",
       "      <td>1.308333</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.203973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.568616</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.470004</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.139434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.568616</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.966113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.458615</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.095310</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.207275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.757858</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.182322</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.427116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.740466</td>\n",
       "      <td>1.481605</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-0.916291</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.510826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.686399</td>\n",
       "      <td>1.360977</td>\n",
       "      <td>0.262364</td>\n",
       "      <td>-0.916291</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.653926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.629241</td>\n",
       "      <td>1.252763</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.203973</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.867501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.740466</td>\n",
       "      <td>1.335001</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>-1.203973</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.673345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.629241</td>\n",
       "      <td>1.335001</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-1.203973</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.798508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.686399</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.078810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.629241</td>\n",
       "      <td>1.308333</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-0.916291</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.510826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.526056</td>\n",
       "      <td>1.280934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.629241</td>\n",
       "      <td>1.193922</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.162519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.568616</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.641854</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.967584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.470004</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.139434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.470004</td>\n",
       "      <td>-0.916291</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.446287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.648659</td>\n",
       "      <td>1.252763</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.203973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.648659</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.272966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.547563</td>\n",
       "      <td>1.163151</td>\n",
       "      <td>0.470004</td>\n",
       "      <td>-1.609438</td>\n",
       "      <td>SET</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.139434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.931521</td>\n",
       "      <td>1.163151</td>\n",
       "      <td>1.740466</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.573375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1.722767</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.589235</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.282382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2.041220</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.902108</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.595255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.840550</td>\n",
       "      <td>0.993252</td>\n",
       "      <td>1.589235</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.177022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>1.902108</td>\n",
       "      <td>1.193922</td>\n",
       "      <td>1.740466</td>\n",
       "      <td>0.741937</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.482404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>1.974081</td>\n",
       "      <td>1.163151</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.379546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.824549</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.568616</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.156403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.808289</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.589235</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.177022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>1.856298</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.722767</td>\n",
       "      <td>0.741937</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.464704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>1.974081</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>0.470004</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.227862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2.001480</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.808289</td>\n",
       "      <td>0.641854</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.450143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2.066863</td>\n",
       "      <td>1.335001</td>\n",
       "      <td>1.856298</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.549445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1.856298</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.722767</td>\n",
       "      <td>0.788457</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.511224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.840550</td>\n",
       "      <td>1.029619</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>0.405465</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.034706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.808289</td>\n",
       "      <td>0.955511</td>\n",
       "      <td>1.722767</td>\n",
       "      <td>0.336472</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.059239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2.041220</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.808289</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.641198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1.840550</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>1.722767</td>\n",
       "      <td>0.875469</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.598235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.856298</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>1.704748</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.292535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.568616</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.156403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1.931521</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>1.686399</td>\n",
       "      <td>0.741937</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.428336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1.902108</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>1.722767</td>\n",
       "      <td>0.875469</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.598235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.931521</td>\n",
       "      <td>1.131402</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.462150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.757858</td>\n",
       "      <td>0.993252</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>0.641854</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.271094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1.916923</td>\n",
       "      <td>1.163151</td>\n",
       "      <td>1.774952</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.607861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>1.902108</td>\n",
       "      <td>1.193922</td>\n",
       "      <td>1.740466</td>\n",
       "      <td>0.916291</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.656757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.902108</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.648659</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.481568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.840550</td>\n",
       "      <td>0.916291</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.641854</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.251292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.871802</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.648659</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.341806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.824549</td>\n",
       "      <td>1.223775</td>\n",
       "      <td>1.686399</td>\n",
       "      <td>0.832909</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.519308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.774952</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.629241</td>\n",
       "      <td>0.587787</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "      <td>2.217027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length  sepal width  petal length  petal width class  wide petal  \\\n",
       "0        1.629241     1.252763      0.336472    -1.609438   SET           0   \n",
       "1        1.589235     1.098612      0.336472    -1.609438   SET           0   \n",
       "2        1.547563     1.163151      0.262364    -1.609438   SET           0   \n",
       "3        1.526056     1.131402      0.405465    -1.609438   SET           0   \n",
       "4        1.609438     1.280934      0.336472    -1.609438   SET           0   \n",
       "5        1.686399     1.360977      0.530628    -0.916291   SET           0   \n",
       "6        1.526056     1.223775      0.336472    -1.203973   SET           0   \n",
       "7        1.609438     1.223775      0.405465    -1.609438   SET           0   \n",
       "8        1.481605     1.064711      0.336472    -1.609438   SET           0   \n",
       "9        1.589235     1.131402      0.405465    -2.302585   SET           0   \n",
       "10       1.686399     1.308333      0.405465    -1.609438   SET           0   \n",
       "11       1.568616     1.223775      0.470004    -1.609438   SET           0   \n",
       "12       1.568616     1.098612      0.336472    -2.302585   SET           0   \n",
       "13       1.458615     1.098612      0.095310    -2.302585   SET           0   \n",
       "14       1.757858     1.386294      0.182322    -1.609438   SET           0   \n",
       "15       1.740466     1.481605      0.405465    -0.916291   SET           0   \n",
       "16       1.686399     1.360977      0.262364    -0.916291   SET           0   \n",
       "17       1.629241     1.252763      0.336472    -1.203973   SET           0   \n",
       "18       1.740466     1.335001      0.530628    -1.203973   SET           0   \n",
       "19       1.629241     1.335001      0.405465    -1.203973   SET           0   \n",
       "20       1.686399     1.223775      0.530628    -1.609438   SET           0   \n",
       "21       1.629241     1.308333      0.405465    -0.916291   SET           0   \n",
       "22       1.526056     1.280934      0.000000    -1.609438   SET           0   \n",
       "23       1.629241     1.193922      0.530628    -0.693147   SET           0   \n",
       "24       1.568616     1.223775      0.641854    -1.609438   SET           0   \n",
       "25       1.609438     1.098612      0.470004    -1.609438   SET           0   \n",
       "26       1.609438     1.223775      0.470004    -0.916291   SET           0   \n",
       "27       1.648659     1.252763      0.405465    -1.609438   SET           0   \n",
       "28       1.648659     1.223775      0.336472    -1.609438   SET           0   \n",
       "29       1.547563     1.163151      0.470004    -1.609438   SET           0   \n",
       "..            ...          ...           ...          ...   ...         ...   \n",
       "120      1.931521     1.163151      1.740466     0.832909   VIR           1   \n",
       "121      1.722767     1.029619      1.589235     0.693147   VIR           1   \n",
       "122      2.041220     1.029619      1.902108     0.693147   VIR           1   \n",
       "123      1.840550     0.993252      1.589235     0.587787   VIR           1   \n",
       "124      1.902108     1.193922      1.740466     0.741937   VIR           1   \n",
       "125      1.974081     1.163151      1.791759     0.587787   VIR           1   \n",
       "126      1.824549     1.029619      1.568616     0.587787   VIR           1   \n",
       "127      1.808289     1.098612      1.589235     0.587787   VIR           1   \n",
       "128      1.856298     1.029619      1.722767     0.741937   VIR           1   \n",
       "129      1.974081     1.098612      1.757858     0.470004   VIR           1   \n",
       "130      2.001480     1.029619      1.808289     0.641854   VIR           1   \n",
       "131      2.066863     1.335001      1.856298     0.693147   VIR           1   \n",
       "132      1.856298     1.029619      1.722767     0.788457   VIR           1   \n",
       "133      1.840550     1.029619      1.629241     0.405465   VIR           1   \n",
       "134      1.808289     0.955511      1.722767     0.336472   VIR           1   \n",
       "135      2.041220     1.098612      1.808289     0.832909   VIR           1   \n",
       "136      1.840550     1.223775      1.722767     0.875469   VIR           1   \n",
       "137      1.856298     1.131402      1.704748     0.587787   VIR           1   \n",
       "138      1.791759     1.098612      1.568616     0.587787   VIR           1   \n",
       "139      1.931521     1.131402      1.686399     0.741937   VIR           1   \n",
       "140      1.902108     1.131402      1.722767     0.875469   VIR           1   \n",
       "141      1.931521     1.131402      1.629241     0.832909   VIR           1   \n",
       "142      1.757858     0.993252      1.629241     0.641854   VIR           1   \n",
       "143      1.916923     1.163151      1.774952     0.832909   VIR           1   \n",
       "144      1.902108     1.193922      1.740466     0.916291   VIR           1   \n",
       "145      1.902108     1.098612      1.648659     0.832909   VIR           1   \n",
       "146      1.840550     0.916291      1.609438     0.641854   VIR           1   \n",
       "147      1.871802     1.098612      1.648659     0.693147   VIR           1   \n",
       "148      1.824549     1.223775      1.686399     0.832909   VIR           1   \n",
       "149      1.774952     1.098612      1.629241     0.587787   VIR           1   \n",
       "\n",
       "     petal area  \n",
       "0     -1.272966  \n",
       "1     -1.272966  \n",
       "2     -1.347074  \n",
       "3     -1.203973  \n",
       "4     -1.272966  \n",
       "5     -0.385662  \n",
       "6     -0.867501  \n",
       "7     -1.203973  \n",
       "8     -1.272966  \n",
       "9     -1.897120  \n",
       "10    -1.203973  \n",
       "11    -1.139434  \n",
       "12    -1.966113  \n",
       "13    -2.207275  \n",
       "14    -1.427116  \n",
       "15    -0.510826  \n",
       "16    -0.653926  \n",
       "17    -0.867501  \n",
       "18    -0.673345  \n",
       "19    -0.798508  \n",
       "20    -1.078810  \n",
       "21    -0.510826  \n",
       "22    -1.609438  \n",
       "23    -0.162519  \n",
       "24    -0.967584  \n",
       "25    -1.139434  \n",
       "26    -0.446287  \n",
       "27    -1.203973  \n",
       "28    -1.272966  \n",
       "29    -1.139434  \n",
       "..          ...  \n",
       "120    2.573375  \n",
       "121    2.282382  \n",
       "122    2.595255  \n",
       "123    2.177022  \n",
       "124    2.482404  \n",
       "125    2.379546  \n",
       "126    2.156403  \n",
       "127    2.177022  \n",
       "128    2.464704  \n",
       "129    2.227862  \n",
       "130    2.450143  \n",
       "131    2.549445  \n",
       "132    2.511224  \n",
       "133    2.034706  \n",
       "134    2.059239  \n",
       "135    2.641198  \n",
       "136    2.598235  \n",
       "137    2.292535  \n",
       "138    2.156403  \n",
       "139    2.428336  \n",
       "140    2.598235  \n",
       "141    2.462150  \n",
       "142    2.271094  \n",
       "143    2.607861  \n",
       "144    2.656757  \n",
       "145    2.481568  \n",
       "146    2.251292  \n",
       "147    2.341806  \n",
       "148    2.519308  \n",
       "149    2.217027  \n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.applymap(lambda v: np.log(v) if isinstance(v, float) else v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>wide petal</th>\n",
       "      <th>petal area</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>5.006</td>\n",
       "      <td>3.418</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VER</th>\n",
       "      <td>5.936</td>\n",
       "      <td>2.770</td>\n",
       "      <td>4.260</td>\n",
       "      <td>1.326</td>\n",
       "      <td>0.7</td>\n",
       "      <td>5.7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIR</th>\n",
       "      <td>6.588</td>\n",
       "      <td>2.974</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.026</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.2962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length  sepal width  petal length  petal width  wide petal  \\\n",
       "class                                                                     \n",
       "SET           5.006        3.418         1.464        0.244         0.0   \n",
       "VER           5.936        2.770         4.260        1.326         0.7   \n",
       "VIR           6.588        2.974         5.552        2.026         1.0   \n",
       "\n",
       "       petal area  \n",
       "class              \n",
       "SET        0.3628  \n",
       "VER        5.7204  \n",
       "VIR       11.2962  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>petal area</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>wide petal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">SET</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.362800</td>\n",
       "      <td>1.464000</td>\n",
       "      <td>0.244000</td>\n",
       "      <td>5.006000</td>\n",
       "      <td>3.418000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.183248</td>\n",
       "      <td>0.173511</td>\n",
       "      <td>0.107210</td>\n",
       "      <td>0.352490</td>\n",
       "      <td>0.381024</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.110000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.265000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>3.675000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">VER</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.720400</td>\n",
       "      <td>4.260000</td>\n",
       "      <td>1.326000</td>\n",
       "      <td>5.936000</td>\n",
       "      <td>2.770000</td>\n",
       "      <td>0.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.368403</td>\n",
       "      <td>0.469911</td>\n",
       "      <td>0.197753</td>\n",
       "      <td>0.516171</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>0.46291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.860000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>2.525000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.615000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.750000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.640000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">VIR</th>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.296200</td>\n",
       "      <td>5.552000</td>\n",
       "      <td>2.026000</td>\n",
       "      <td>6.588000</td>\n",
       "      <td>2.974000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.157412</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>0.274650</td>\n",
       "      <td>0.635880</td>\n",
       "      <td>0.322497</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.717500</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.445000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.790000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>3.175000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.870000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             petal area  petal length  petal width  sepal length  sepal width  \\\n",
       "class                                                                           \n",
       "SET   count   50.000000     50.000000    50.000000     50.000000    50.000000   \n",
       "      mean     0.362800      1.464000     0.244000      5.006000     3.418000   \n",
       "      std      0.183248      0.173511     0.107210      0.352490     0.381024   \n",
       "      min      0.110000      1.000000     0.100000      4.300000     2.300000   \n",
       "      25%      0.265000      1.400000     0.200000      4.800000     3.125000   \n",
       "      50%      0.300000      1.500000     0.200000      5.000000     3.400000   \n",
       "      75%      0.420000      1.575000     0.300000      5.200000     3.675000   \n",
       "      max      0.960000      1.900000     0.600000      5.800000     4.400000   \n",
       "VER   count   50.000000     50.000000    50.000000     50.000000    50.000000   \n",
       "      mean     5.720400      4.260000     1.326000      5.936000     2.770000   \n",
       "      std      1.368403      0.469911     0.197753      0.516171     0.313798   \n",
       "      min      3.300000      3.000000     1.000000      4.900000     2.000000   \n",
       "      25%      4.860000      4.000000     1.200000      5.600000     2.525000   \n",
       "      50%      5.615000      4.350000     1.300000      5.900000     2.800000   \n",
       "      75%      6.750000      4.600000     1.500000      6.300000     3.000000   \n",
       "      max      8.640000      5.100000     1.800000      7.000000     3.400000   \n",
       "VIR   count   50.000000     50.000000    50.000000     50.000000    50.000000   \n",
       "      mean    11.296200      5.552000     2.026000      6.588000     2.974000   \n",
       "      std      2.157412      0.551895     0.274650      0.635880     0.322497   \n",
       "      min      7.500000      4.500000     1.400000      4.900000     2.200000   \n",
       "      25%      9.717500      5.100000     1.800000      6.225000     2.800000   \n",
       "      50%     11.445000      5.550000     2.000000      6.500000     3.000000   \n",
       "      75%     12.790000      5.875000     2.300000      6.900000     3.175000   \n",
       "      max     15.870000      6.900000     2.500000      7.900000     3.800000   \n",
       "\n",
       "             wide petal  \n",
       "class                    \n",
       "SET   count    50.00000  \n",
       "      mean      0.00000  \n",
       "      std       0.00000  \n",
       "      min       0.00000  \n",
       "      25%       0.00000  \n",
       "      50%       0.00000  \n",
       "      75%       0.00000  \n",
       "      max       0.00000  \n",
       "VER   count    50.00000  \n",
       "      mean      0.70000  \n",
       "      std       0.46291  \n",
       "      min       0.00000  \n",
       "      25%       0.00000  \n",
       "      50%       1.00000  \n",
       "      75%       1.00000  \n",
       "      max       1.00000  \n",
       "VIR   count    50.00000  \n",
       "      mean      1.00000  \n",
       "      std       0.00000  \n",
       "      min       1.00000  \n",
       "      25%       1.00000  \n",
       "      50%       1.00000  \n",
       "      75%       1.00000  \n",
       "      max       1.00000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>[SET]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>[VER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.1</th>\n",
       "      <td>[VER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>[VER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.3</th>\n",
       "      <td>[VER]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.4</th>\n",
       "      <td>[VER, VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>[VER, VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6</th>\n",
       "      <td>[VER, VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.7</th>\n",
       "      <td>[VER, VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.8</th>\n",
       "      <td>[VER, VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.9</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.1</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.2</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.3</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.4</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>[VIR]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  class\n",
       "petal width            \n",
       "0.1               [SET]\n",
       "0.2               [SET]\n",
       "0.3               [SET]\n",
       "0.4               [SET]\n",
       "0.5               [SET]\n",
       "0.6               [SET]\n",
       "1.0               [VER]\n",
       "1.1               [VER]\n",
       "1.2               [VER]\n",
       "1.3               [VER]\n",
       "1.4          [VER, VIR]\n",
       "1.5          [VER, VIR]\n",
       "1.6          [VER, VIR]\n",
       "1.7          [VER, VIR]\n",
       "1.8          [VER, VIR]\n",
       "1.9               [VIR]\n",
       "2.0               [VIR]\n",
       "2.1               [VIR]\n",
       "2.2               [VIR]\n",
       "2.3               [VIR]\n",
       "2.4               [VIR]\n",
       "2.5               [VIR]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('petal width')['class'].unique().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>delta</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SET</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VER</th>\n",
       "      <td>1.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIR</th>\n",
       "      <td>2.5</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       max  delta  min\n",
       "class                 \n",
       "SET    0.6    0.5  0.1\n",
       "VER    1.8    0.8  1.0\n",
       "VIR    2.5    1.1  1.4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class')['petal width']\\\n",
    ".agg({'delta': lambda x: x.max() - x.min(), 'max': np.max, 'min': np.min})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10fb527f0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Vah+p1odFRETgww8/xEcffYSQkBC4urrK8z7++GO0a9cOvr6+MJlM2LlzJ7y8vOQgjImJ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\nLxyaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f9d24a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.scatter(df['sepal width'][:50], df['sepal length'][:50])\n",
    "ax.set_ylabel('Sepal Length')\n",
    "ax.set_xlabel('Sepal Width')\n",
    "ax.set_title('Setosa Sepal Width vs. Sepal Length', fontsize=14, y=1.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           sepal length   R-squared:                       0.558\n",
      "Model:                            OLS   Adj. R-squared:                  0.548\n",
      "Method:                 Least Squares   F-statistic:                     60.52\n",
      "Date:                Sun, 07 Feb 2016   Prob (F-statistic):           4.75e-10\n",
      "Time:                        19:18:54   Log-Likelihood:                 2.0879\n",
      "No. Observations:                  50   AIC:                           -0.1759\n",
      "Df Residuals:                      48   BIC:                             3.648\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "-------------------------------------------------------------------------------\n",
      "const           2.6447      0.305      8.660      0.000         2.031     3.259\n",
      "sepal width     0.6909      0.089      7.779      0.000         0.512     0.869\n",
      "==============================================================================\n",
      "Omnibus:                        0.252   Durbin-Watson:                   2.517\n",
      "Prob(Omnibus):                  0.882   Jarque-Bera (JB):                0.436\n",
      "Skew:                          -0.110   Prob(JB):                        0.804\n",
      "Kurtosis:                       2.599   Cond. No.                         34.0\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "y = df['sepal length'][:50]\n",
    "x = df['sepal width'][:50]\n",
    "X = sm.add_constant(x)\n",
    "\n",
    "results = sm.OLS(y, X).fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x110528d30>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N1Lhx4+QugYjI7DAMich0qdVAVJTmeVAQoFTKWQ2ZMIYhEZkmtRrw9wdiYjTt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\nSTEUQgiheVIMhRBCaJ4UQyGEEJr3H25L8heKdM73AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103c9588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.plot(x, results.fittedvalues, label='regression line')\n",
    "ax.scatter(x, y, label='data point', color='r')\n",
    "ax.set_ylabel('Sepal Length')\n",
    "ax.set_xlabel('Sepal Width')\n",
    "ax.set_title('Setosa Sepal Width vs. Sepal Length', fontsize=14, y=1.02)\n",
    "ax.legend(loc=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>actual</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VER</td>\n",
       "      <td>VIR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>VER</td>\n",
       "      <td>VIR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VER</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted actual  correct\n",
       "0        VER    VER        1\n",
       "1        VIR    VIR        1\n",
       "2        VIR    VIR        1\n",
       "3        VIR    VIR        1\n",
       "4        VER    VIR        0\n",
       "5        VER    VER        1\n",
       "6        VIR    VIR        1\n",
       "7        VIR    VIR        1\n",
       "8        VER    VER        1\n",
       "9        VIR    VIR        1\n",
       "10       VER    VER        1\n",
       "11       SET    SET        1\n",
       "12       VER    VER        1\n",
       "13       VIR    VIR        1\n",
       "14       VIR    VIR        1\n",
       "15       VIR    VIR        1\n",
       "16       VIR    VIR        1\n",
       "17       SET    SET        1\n",
       "18       VIR    VIR        1\n",
       "19       VER    VER        1\n",
       "20       SET    SET        1\n",
       "21       VER    VER        1\n",
       "22       VER    VER        1\n",
       "23       VER    VER        1\n",
       "24       SET    SET        1\n",
       "25       SET    SET        1\n",
       "26       VER    VER        1\n",
       "27       VER    VER        1\n",
       "28       VER    VER        1\n",
       "29       SET    SET        1\n",
       "30       VER    VER        1\n",
       "31       VER    VER        1\n",
       "32       SET    SET        1\n",
       "33       VIR    VIR        1\n",
       "34       SET    SET        1\n",
       "35       VIR    VIR        1\n",
       "36       SET    SET        1\n",
       "37       VIR    VIR        1\n",
       "38       VER    VIR        0\n",
       "39       SET    SET        1\n",
       "40       VER    VER        1\n",
       "41       VIR    VER        0\n",
       "42       VIR    VIR        1\n",
       "43       SET    SET        1\n",
       "44       VER    VER        1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=5, n_estimators=10)\n",
    "\n",
    "X = df.ix[:,:4]\n",
    "y = df.ix[:,4]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "rf = pd.DataFrame(list(zip(y_pred, y_test)), columns=['predicted', 'actual'])\n",
    "rf['correct'] = rf.apply(lambda r: 1 if r['predicted'] == r['actual'] else 0, axis=1)\n",
    "    \n",
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93333333333333335"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf['correct'].sum()/rf['correct'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GlZSUFF72er2qqak55viXX35Zfr/faVkAAGKK40PiJ+Odd95RSUmJ/v73v5/KsgAAWM9x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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11135ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_importances = clf.feature_importances_\n",
    "f_names = df.columns[:4]\n",
    "f_std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)\n",
    "\n",
    "zz = zip(f_importances, f_names, f_std)\n",
    "zzs = sorted(zz, key=lambda x: x[0], reverse=True)\n",
    "\n",
    "imps = [x[0] for x in zzs]\n",
    "labels = [x[1] for x in zzs]\n",
    "errs = [x[2] for x in zzs]\n",
    "\n",
    "plt.bar(range(len(f_importances)), imps, color=\"r\", yerr=errs, align=\"center\")\n",
    "plt.xticks(range(len(f_importances)), labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>actual</th>\n",
       "      <th>correct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>VIR</td>\n",
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       "      <th>13</th>\n",
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       "      <td>1</td>\n",
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       "      <th>17</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
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       "      <th>18</th>\n",
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       "      <td>VIR</td>\n",
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       "      <th>20</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
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       "      <td>VER</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>VER</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>VER</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VER</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
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       "      <th>30</th>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>VIR</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>VIR</td>\n",
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       "      <th>35</th>\n",
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       "      <td>VER</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
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       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VER</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>VER</td>\n",
       "      <td>VER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SET</td>\n",
       "      <td>SET</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VIR</td>\n",
       "      <td>VIR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   predicted actual  correct\n",
       "0        VIR    VIR        1\n",
       "1        VIR    VIR        1\n",
       "2        VIR    VIR        1\n",
       "3        VIR    VIR        1\n",
       "4        VIR    VIR        1\n",
       "5        SET    SET        1\n",
       "6        VER    VER        1\n",
       "7        VER    VER        1\n",
       "8        VER    VER        1\n",
       "9        VIR    VER        0\n",
       "10       SET    SET        1\n",
       "11       VIR    VIR        1\n",
       "12       VER    VER        1\n",
       "13       VIR    VIR        1\n",
       "14       VER    VER        1\n",
       "15       VER    VER        1\n",
       "16       VER    VER        1\n",
       "17       SET    SET        1\n",
       "18       VER    VIR        0\n",
       "19       VER    VER        1\n",
       "20       VIR    VIR        1\n",
       "21       VER    VER        1\n",
       "22       SET    SET        1\n",
       "23       SET    SET        1\n",
       "24       SET    SET        1\n",
       "25       VER    VER        1\n",
       "26       VER    VER        1\n",
       "27       VIR    VER        0\n",
       "28       VER    VER        1\n",
       "29       SET    SET        1\n",
       "30       VIR    VIR        1\n",
       "31       VIR    VIR        1\n",
       "32       SET    SET        1\n",
       "33       VIR    VIR        1\n",
       "34       VIR    VIR        1\n",
       "35       VER    VER        1\n",
       "36       VER    VER        1\n",
       "37       VIR    VIR        1\n",
       "38       VIR    VIR        1\n",
       "39       SET    SET        1\n",
       "40       VIR    VIR        1\n",
       "41       VIR    VER        0\n",
       "42       VER    VER        1\n",
       "43       SET    SET        1\n",
       "44       VIR    VIR        1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "clf = OneVsRestClassifier(SVC(kernel='linear'))\n",
    "\n",
    "X = df.ix[:,:4]\n",
    "y = np.array(df.ix[:,4]).astype(str)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "rf = pd.DataFrame(list(zip(y_pred, y_test)), columns=['predicted', 'actual'])\n",
    "rf['correct'] = rf.apply(lambda r: 1 if r['predicted'] == r['actual'] else 0, axis=1)\n",
    "    \n",
    "rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91111111111111109"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf['correct'].sum()/rf['correct'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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